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  • Title: Rapid identification of highly active and selective substrates for stromelysin and matrilysin using bacteriophage peptide display libraries.
    Author: Smith MM, Shi L, Navre M.
    Journal: J Biol Chem; 1995 Mar 24; 270(12):6440-9. PubMed ID: 7896777.
    Abstract:
    The discovery of useful peptide substrates for proteases that recognize many amino acids in their active sites is often a slow process due to the lack of initial substrate data and the expense of analyzing large numbers of peptide substrates. To overcome these obstacles, we have made use of bacteriophage peptide display libraries. We prepared a random hexamer library in the fd-derived vector fAFF-1 and included a "tether" sequence that could be recognized by monoclonal antibodies. We chose the matrix metalloproteinases stromelysin and matrilysin as the targets for our studies, as they are known to require at least 6 amino acids in a peptide substrate for cleavage. The phage library was treated in solution with protease and cleaved phage separated from uncleaved phage using a mixture of tether-binding monoclonal antibodies and Protein A-bearing cells followed by precipitation. Clones were screened by the use of a rapid screening assay that identified phage encoding peptide sequences susceptible to cleavage by the enzymes. The nucleotide sequence of the random hexamer region of 43 such clones was determined for stromelysin and 23 for matrilysin. Synthetic peptides were prepared whose sequences were based on some of the positive clones, as well as consensus sequences built from the positive clones. Many of the peptides have kcat/KM values as good or better than those of previously reported substrates, and in fact, we were able to produce stromelysin and matrilysin substrates that are both the most active and smallest reported to date. In addition, the phage data predicted selectivity in the P2 and P'1 positions of the two enzymes that were supported by the kinetic analysis of the peptides. This work demonstrates that the phage selection techniques enable the rapid identification of highly active and selective protease substrates without making any a priori assumptions about the specificity or the "physiological substrate" of the protease under study.
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